Note: The job is a remote job and is open to candidates in USA. RiskSpan is a leading source of analytics, modeling, data, and risk management solutions for the Consumer and Institutional Finance industries. The AI Engineer will design, build, and deploy production-grade AI applications, focusing on integrating enterprise data and delivering scalable solutions in AWS.
Responsibilities
- Design, build, and deploy AI-powered applications including chatbots, knowledge assistants, and workflow automation agents
- Implement end-to-end solutions covering data ingestion, transformation, prompt orchestration, model interaction, and cloud deployment
- Integrate AI systems with internal APIs, enterprise platforms, and data pipelines
- Design agent workflows with tool/function calling, branching logic, retries, and fallback handling
- Implement human-in-the-loop and approval-based workflows for regulated financial use cases
- Build multi-agent systems for validation, refinement, and complex task decomposition
- Design and implement RAG pipelines covering chunking, embeddings, retrieval, and grounding
- Work with structured and unstructured data using SQL, S3, and data pipeline tools
- Leverage AWS services (S3, Glue, Redshift, Lambda, ECS, Step Functions, SQS/SNS) for storage, transformation, and orchestration
- Monitor and improve AI systems for accuracy, latency, cost, and reliability
- Implement structured output validation, schema enforcement, and guardrails
- Evaluate model performance and iteratively improve grounding and output consistency
Skills
- Strong experience building AI applications using LLMs (e.g., AWS Bedrock or equivalent platforms)
- Hands-on experience with RAG architectures and retrieval pipelines
- Experience with vector databases, embeddings, and semantic search
- Demonstrated track record deploying production AI systems end-to-end — not just prototypes
- Solid Python programming skills (required)
- Experience with core AWS services: Lambda, ECS, S3, Step Functions, SQS/SNS
- Strong SQL skills for querying and integrating structured data
- Experience integrating AI systems with APIs, databases, and cloud services
- Understanding of prompt engineering, tool/function calling, and structured outputs
- Strong problem-solving skills for building reliable systems around probabilistic AI behavior
- Experience with AWS Bedrock AgentCore or similar agent orchestration frameworks
- Experience building multi-agent systems or advanced agent workflows
- Experience with AWS Glue, Redshift, EMR, or broader data engineering pipelines
- Experience with LLM evaluation frameworks and automated testing
- Knowledge of schema validation, guardrails, and output control techniques
- Experience with CI/CD, containerization, and infrastructure as code
- Background in financial services, regulated environments, or GSE/enterprise data platforms
Company Overview